Add streaming modified beam search

.github/workflows/run-streaming-conformer-test.yaml

@@ -52,7 +52,7 @@ jobs:
         torch: ["1.10.0", "1.6.0"]
         torchaudio: ["0.10.0", "0.6.0"]
         python-version: ["3.7", "3.8"]
-        decoding: ["greedy_search", "fast_beam_search", "fast_beam_search_nbest", "fast_beam_search_nbest_LG"]
+        decoding: ["greedy_search", "fast_beam_search", "fast_beam_search_nbest", "fast_beam_search_nbest_LG", "modified_beam_search"]
         exclude:
           - torch: "1.10.0"
             torchaudio: "0.6.0"

.github/workflows/run-streaming-conv-emformer-test.yaml

@@ -52,7 +52,7 @@ jobs:
         torch: ["1.10.0", "1.6.0"]
         torchaudio: ["0.10.0", "0.6.0"]
         python-version: ["3.7", "3.8"]
-        decoding: ["greedy_search", "fast_beam_search", "fast_beam_search_nbest", "fast_beam_search_nbest_LG"]
+        decoding: ["greedy_search", "fast_beam_search", "fast_beam_search_nbest", "fast_beam_search_nbest_LG", "modified_beam_search"]
         exclude:
           - torch: "1.10.0"
             torchaudio: "0.6.0"

.github/workflows/run-wenetspeech-streaming-conformer-rnnt-test.yaml

@@ -52,7 +52,7 @@ jobs:
         torch: ["1.11.0", "1.7.1"]
         torchaudio: ["0.11.0", "0.7.2"]
         python-version: ["3.7", "3.8"]
-        decoding: ["greedy_search", "fast_beam_search", "fast_beam_search_nbest", "fast_beam_search_nbest_LG"]
+        decoding: ["greedy_search", "fast_beam_search", "fast_beam_search_nbest", "fast_beam_search_nbest_LG", "modified_beam_search"]
         exclude:
           - torch: "1.11.0"
             torchaudio: "0.7.2"

sherpa/bin/conv_emformer_transducer_stateless2/beam_search.py

@@ -6,11 +6,14 @@
 
 from sherpa import (
     VALID_FAST_BEAM_SEARCH_METHOD,
+    Hypotheses,
+    Hypothesis,
     Lexicon,
     fast_beam_search_nbest,
     fast_beam_search_nbest_LG,
     fast_beam_search_one_best,
     streaming_greedy_search,
+    streaming_modified_beam_search,
 )
 
 
@@ -339,3 +342,111 @@ def get_texts(self, stream: Stream) -> str:
         return self.sp.decode(
             stream.hyp[self.beam_search_params["context_size"] :]
         )
+
+
+class ModifiedBeamSearch:
+    def __init__(self, beam_search_params: dict):
+        self.beam_search_params = beam_search_params
+
+    def init_stream(self, stream: Stream):
+        """
+        Attributes to add to each stream
+        """
+        hyp = [self.beam_search_params["blank_id"]] * self.beam_search_params[
+            "context_size"
+        ]
+        stream.hyps = Hypotheses([Hypothesis(ys=hyp, log_prob=0.0)])
+
+    @torch.no_grad()
+    def process(
+        self,
+        server: "StreamingServer",
+        stream_list: List[Stream],
+    ) -> None:
+        """Run the model on the given stream list and do search with greedy_search
+           method.
+        Args:
+          server:
+            An instance of `StreamingServer`.
+          stream_list:
+            A list of streams to be processed. It is changed in-place.
+            That is, the attribute `states` and `hyp` are
+            updated in-place.
+        """
+        model = server.model
+        device = model.device
+        # Note: chunk_length is in frames before subsampling
+        chunk_length = server.chunk_length
+        batch_size = len(stream_list)
+        chunk_length_pad = server.chunk_length_pad
+        state_list, feature_list = [], []
+        hyp_list = []
+        processed_frames_list = []
+        num_trailing_blank_frames_list = []
+
+        for s in stream_list:
+            hyp_list.append(s.hyps)
+            state_list.append(s.states)
+            processed_frames_list.append(s.processed_frames)
+            f = s.features[:chunk_length_pad]
+            s.features = s.features[chunk_length:]
+            s.processed_frames += chunk_length
+
+            b = torch.cat(f, dim=0)
+            feature_list.append(b)
+
+            num_trailing_blank_frames_list.append(s.num_trailing_blank_frames)
+
+        features = torch.stack(feature_list, dim=0).to(device)
+        states = stack_states(state_list)
+
+        features_length = torch.full(
+            (batch_size,),
+            fill_value=features.size(1),
+            device=device,
+            dtype=torch.int64,
+        )
+
+        num_processed_frames = torch.tensor(
+            processed_frames_list,
+            device=device,
+        )
+
+        (
+            encoder_out,
+            encoder_out_lens,
+            next_states,
+        ) = model.encoder_streaming_forward(
+            features=features,
+            features_length=features_length,
+            num_processed_frames=num_processed_frames,
+            states=states,
+        )
+
+        # Note: There are no paddings for streaming ASR. Each stream
+        # has the same input number of frames, i.e., server.chunk_length.
+        next_hyps_list = streaming_modified_beam_search(
+            model=model,
+            encoder_out=encoder_out,
+            hyps=hyp_list,
+            num_active_paths=self.beam_search_params["num_active_paths"],
+        )
+
+        next_state_list = unstack_states(next_states)
+        for i, s in enumerate(stream_list):
+            s.states = next_state_list[i]
+            s.hyps = next_hyps_list[i]
+            trailing_blanks = s.hyps.get_most_probable(True).num_trailing_blanks
+            s.num_trailing_blank_frames = trailing_blanks
+
+    def get_texts(self, stream: Stream) -> str:
+        hyp = stream.hyps.get_most_probable(True).ys[
+            self.beam_search_params["context_size"] :
+        ]
+        if hasattr(self, "sp"):
+            result = self.sp.decode(hyp)
+        else:
+            result = [self.token_table[i] for i in hyp]
+            result = "".join(result)
+
+        return result

sherpa/bin/conv_emformer_transducer_stateless2/streaming_server.py

@@ -45,7 +45,7 @@
 import sentencepiece as spm
 import torch
 import websockets
-from beam_search import FastBeamSearch, GreedySearch
+from beam_search import FastBeamSearch, GreedySearch, ModifiedBeamSearch
 from stream import Stream
 
 from sherpa import (
@@ -275,6 +275,8 @@ def __init__(
                 beam_search_params,
                 device,
             )
+        elif decoding_method == "modified_beam_search":
+            self.beam_search = ModifiedBeamSearch(beam_search_params)
         else:
             raise ValueError(
                 f"Decoding method {decoding_method} is not supported."

sherpa/bin/streaming_pruned_transducer_statelessX/beam_search.py

@@ -6,11 +6,14 @@
 
 from sherpa import (
     VALID_FAST_BEAM_SEARCH_METHOD,
+    Hypotheses,
+    Hypothesis,
     Lexicon,
     fast_beam_search_nbest,
     fast_beam_search_nbest_LG,
     fast_beam_search_one_best,
     streaming_greedy_search,
+    streaming_modified_beam_search,
 )
 
 
@@ -378,3 +381,121 @@ def get_texts(self, stream: Stream) -> str:
             result = "".join(result).replace("▁", " ")
 
         return result
+
+
+class ModifiedBeamSearch:
+    def __init__(self, beam_search_params: dict):
+        self.beam_search_params = beam_search_params
+
+    def init_stream(self, stream: Stream):
+        """
+        Attributes to add to each stream
+        """
+        hyp = [self.beam_search_params["blank_id"]] * self.beam_search_params[
+            "context_size"
+        ]
+        stream.hyps = Hypotheses([Hypothesis(ys=hyp, log_prob=0.0)])
+
+    @torch.no_grad()
+    def process(
+        self,
+        server: "StreamingServer",
+        stream_list: List[Stream],
+    ) -> None:
+        """Run the model on the given stream list and do modified_beam_search.
+        Args:
+          server:
+            An instance of `StreamingServer`.
+          stream_list:
+            A list of streams to be processed. It is changed in-place.
+            That is, the attribute `states` and `hyps` are
+            updated in-place.
+        """
+        model = server.model
+        device = model.device
+        # Note: chunk_length is in frames before subsampling
+        chunk_length = server.chunk_length
+        subsampling_factor = server.subsampling_factor
+        # Note: chunk_size, left_context and right_context are in frames
+        # after subsampling
+        chunk_size = server.decode_chunk_size
+        left_context = server.decode_left_context
+        right_context = server.decode_right_context
+
+        batch_size = len(stream_list)
+
+        state_list, feature_list, processed_frames_list = [], [], []
+        hyp_list = []
+
+        num_trailing_blank_frames_list = []
+
+        for s in stream_list:
+            hyp_list.append(s.hyps)
+            state_list.append(s.states)
+            processed_frames_list.append(s.processed_frames)
+            f = s.features[:chunk_length]
+            s.features = s.features[chunk_size * subsampling_factor :]
+            b = torch.cat(f, dim=0)
+            feature_list.append(b)
+
+            num_trailing_blank_frames_list.append(s.num_trailing_blank_frames)
+
+        features = torch.stack(feature_list, dim=0).to(device)
+
+        states = [
+            torch.stack([x[0] for x in state_list], dim=2),
+            torch.stack([x[1] for x in state_list], dim=2),
+        ]
+
+        features_length = torch.full(
+            (batch_size,),
+            fill_value=features.size(1),
+            device=device,
+            dtype=torch.int64,
+        )
+
+        processed_frames = torch.tensor(processed_frames_list, device=device)
+
+        (
+            encoder_out,
+            encoder_out_lens,
+            next_states,
+        ) = model.encoder_streaming_forward(
+            features=features,
+            features_length=features_length,
+            states=states,
+            processed_frames=processed_frames,
+            left_context=left_context,
+            right_context=right_context,
+        )
+
+        next_hyps_list = streaming_modified_beam_search(
+            model=model,
+            encoder_out=encoder_out,
+            hyps=hyp_list,
+            num_active_paths=self.beam_search_params["num_active_paths"],
+        )
+
+        next_state_list = [
+            torch.unbind(next_states[0], dim=2),
+            torch.unbind(next_states[1], dim=2),
+        ]
+
+        for i, s in enumerate(stream_list):
+            s.states = [next_state_list[0][i], next_state_list[1][i]]
+            s.processed_frames += encoder_out_lens[i]
+            s.hyps = next_hyps_list[i]
+            trailing_blanks = s.hyps.get_most_probable(True).num_trailing_blanks
+            s.num_trailing_blank_frames = trailing_blanks
+
+    def get_texts(self, stream: Stream) -> str:
+        hyp = stream.hyps.get_most_probable(True).ys[
+            self.beam_search_params["context_size"] :
+        ]
+        if hasattr(self, "sp"):
+            result = self.sp.decode(hyp)
+        else:
+            result = [self.token_table[i] for i in hyp]
+            result = "".join(result)
+
+        return result

sherpa/bin/streaming_pruned_transducer_statelessX/streaming_server.py

@@ -47,7 +47,7 @@
 import sentencepiece as spm
 import torch
 import websockets
-from beam_search import FastBeamSearch, GreedySearch
+from beam_search import FastBeamSearch, GreedySearch, ModifiedBeamSearch
 from stream import Stream
 
 from sherpa import (
@@ -313,6 +313,8 @@ def __init__(
                 beam_search_params,
                 device,
             )
+        elif decoding_method == "modified_beam_search":
+            self.beam_search = ModifiedBeamSearch(beam_search_params)
         else:
             raise ValueError(
                 f"Decoding method {decoding_method} is not supported."